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DPO vs GRPO vs RL for Reasoning
DPO vs GRPO vs RL for Reasoning
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Intermediate
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Question 1 of 3
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intermediate (5/10)
conceptual
RL for reasoning (e.g., DeepSeek-R1, OpenAI o1) uses VERIFIABLE rewards. Why is this important?
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A.
Verifiable rewards eliminate the need for a frozen reference model and the KL penalty against it, unlike standard RLHF-style fine-tuning.
B.
Math and code have programmatically checkable answers (unit tests, computation), so reward is unambiguous and immune to reward-model errors.
C.
Verifiable rewards require ground-truth labels for every single training example, unlike RLHF which can train from completely unlabeled data.
D.
Verifiable rewards are produced by lightweight neural networks trained alongside the policy, making them faster to score than rule-based pipelines.
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